Automated Assessment of Neurodevelopment in Infants at Risk for Motor Disability

项目摘要/摘要 这个R01项目的总体目标是开发一个自动化评估系统，该系统可以利用 ART传感技术和机器学习算法，实现对婴儿的准确和早期检测 有神经发育障碍的风险。在美国，十分之一的婴儿出生时就有这些残疾的风险。 对于患有神经发育障碍的儿童，在生命的第一年进行早期治疗可以改善长期疗效 结果。然而，我们目前受制于现有临床测试的不足，以衡量和 预测损伤。现有的测试很难管理，需要专门的培训，而且长期有效 术语预测值。迫切需要开发一种客观、准确、易于使用的早期工具 对长期身体残疾的预测。儿科和婴儿发育领域将受益匪浅 根据与目前用于检测运动的现有临床测量相关联的量化分数 在非常小的婴儿中的损害。为了实现易于管理的新一代测试，我们将 获取婴儿在仪表式健身房玩耍或只是在移动时被记录的大型数据集 仰卧姿势。视频和传感器数据分析将基于我们的 问题领域的知识。我们的方法将使用机器学习将这些特征向量关联到 目前推荐的临床测试或其他基本事实信息。这种设计的威力在于 算法可以利用运动的许多方面来产生相关的分数。我们的初步数据显示 美国将制定以下目标：1)目标1：评估多模式工具的并发有效性 健身房有现有的临床工具。在这里，我们使用了150名婴儿(75名早期脑损伤和75名对照) 将专注于将仪表化健身房的数据转换为标准临床测试的估计；2)目标2： 开发一种基于计算机视觉的算法来量化婴儿的运动能力 单摄像头视频。这里使用了1200名婴儿(400名早期脑损伤，400名早产)的视频数据 没有早期脑损伤，400个对照)，加上从目标1和目标3收集的数据，我们将提取姿势数据 并将其转换为运动特征和所需的相关分数 对婴幼儿运动进行分类；3)目的：发现与婴幼儿长期运动发育相关的特征。 在这里，我们将转换从50名婴儿(25名早期脑损伤和25名对照组)纵向收集的数据。 使用仪器健身房和视频记录来估计标准临床测试随着时间的推移而变化 跟踪发育时间范围内的特征。这三个目标带头使用真实世界的行为来 动作得分。我们的目标将使我们更接近于一种通用的非侵入性测试，用于早期检测 并为长期预测残疾奠定了基础。但最重要的是，它 承诺在全球范围内推广到婴儿，生产一种负担得起的工具来帮助婴儿健康评估。

PROJECT SUMMARY/ABSTRACT The overall goal of this R01 project is to develop an automated assessment system that can capitalize on state of the art sensing technologies and machine learning algorithms to enable accurate and early detection of infants at risk for neurodevelopmental disabilities. In the USA, 1 in 10 infants are born at risk for these disabilities. For children with neurodevelopmental disabilities, early treatment in the first year of life improves long-term outcomes. However, we are currently held back by inadequacies of available clinical tests to measure and predict impairment. Existing tests are hard to administer, require specialized training, and have limited long- term predictive value. There is a critical need to develop an objective, accurate, easy-to-use tool for the early prediction of long-term physical disability. The field of pediatrics and infant development would greatly benefit from a quantitative score that would correlate with existing clinical measures used today to detect movement impairments in very young infants. To realize a new generation of tests that will be easy to administer, we will obtain large datasets of infants playing in an instrumented gym or simply being recorded while moving in a supine posture. Video and sensor data analyses will convert movement into feature vectors based on our knowledge of the problem domain. Our approach will use machine learning to relate these feature vectors to currently recommended clinical tests or other ground truth information. The power of this design is that algorithms can utilize many aspects of movement to produce the relevant scores. Our preliminary data allows us to lay the following aims: 1)Aim 1: To assess concurrent validity of a multimodal instrumented gym with existing clinical tools. Here, using 150 infants (75 with early brain injury and 75 controls), we will focus on converting data from an instrumented gym into estimates of the standard clinical tests; 2)Aim 2: To develop a computer vision-based algorithm to quantify infant motor performance from single camera video. Here using video data from 1200 infants (400 with early brain injury, 400 preterm without early brain injury, 400 controls), plus those gathered from Aim 1 and Aim 3, we will extract pose data from single-camera video recordings and convert these into kinematic features and relevant scores needed to classify infant movement; 3)Aim3: To discover the features related to long-term motor development. Here we will convert data collected longitudinally from 50 infants (25 with early brain injury and 25 controls) using both instrumented gym and video recordings into estimates standard clinical tests change over time and track features over developmental timescales. These three aims spearhead the use of real world behavior for movement scoring. Our aims will bring us closer to a universal non-invasive test for early detection of neurodevelopmental disabilities and lay the groundwork for long-term prediction of disability. But above all, it promises to scale to infants worldwide, producing an affordable tool to aid in infant health assessment.